JamesBarney

Yeah I've seen this a lot. People freaking about issues that have small or moderate impact on deadlines, and very little to no revenue impact. And suddenly you're moving 50% slower because you have weeks of review processes to make sure nothing is missed.

Trust them completely != "assumed that we were competent"

I've worked with plenty of competent people. Only a few were so good I trusted them completely.

It's more mixed. They like it because shields them from frivolous complaints, and lets them document evidence against subjects.

They don't like it when management uses it for frivolous minor policy enforcement or they feel like it reduces their ability to use discretion.

But overall police officers who are given cameras tend to me more positive after using them.

They're market makers who provide liquidity. So it's not exactly zero-sum.

How long was the text? And mind sharing?

This should really be the #1 comment on this post.

This really depends what you mean. If you spend $5.00 on anthropic tokens does that cost Anthropic more than $5.00 to serve? No.

If you amortize all of their training and salaries over that $5.00 then yes.

If you only amortize the training costs of that specific model then again we're back to no.

Almost every. Pangram is pretty accurate on longer texts. I haven't seen any glaring examples of false positives.

Honestly academic writing is the only place where I think AI slop might be an improvement over the status quo writing style.

I don't know if Ed is far off the mark. But this article does nothing to help illuminate it.

He mixes estimated capex spend by like 3 different sources with actually commitments by the LLM providers.

He talks about how crazy it would be for ai providers to double revenue every year. But openai is doubling every 9 months and anthropic is doubling every 3.

It's obvious if AI consumption stops growing today those companies are in trouble, and if AI consumption keeps growing at current rates they'll be more than fine.

Most people expect growth rate to slow, just no one knows by how much. This will determine if there is an over build out or not.

The old school solution to this is have different schema in your database, and have views as the cross team public interface.

The issue wasn't sharing a database, it was not being clear about who owns what.

Having multiple teams with one code base that has one database is fine. Every every line of code, table and column needs to be owned by exactly ONE team.

Ownership is the most important part of making an organization effective.

The vast majority of people who write don't have a voice worth preserving. The rest can build out a voice document to make sure the AI doesn't strip it out.

If AI wrote and thought better by default then I wouldn't have to read the AI slop my co-workers send me.

My experience has been

(ordered from best to worst)

1. Author using AI well

2. Author not using AI

3. Author using AI poorly

With the gap between 1 and 2 being driven by the underlying quality of the writer and how well they use AI. A really good writer sees marginal improvements and a really poor one can see vast improvements.